scholarly journals Fire behavior modeling based on simulated field plots

2012 ◽  
Author(s):  
A. Beutling ◽  
A. C. Batista ◽  
R. V. Soares
Keyword(s):  
Fire Behavior ◽  
Behavior Modeling ◽  
Fire Behavior Modeling ◽  
Simulated Field ◽  
Field Plots

scholarly journals Modeling sub-boreal forest canopy bulk density in Minnesota, USA, using synthetic aperture radar and optical satellite sensor data

Fire Ecology ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Peter T. Wolter ◽  
Jacob J. Olbrich ◽  
Patricia J. Johnson
Keyword(s):  
Fire Behavior ◽  
Ground Level ◽  
Behavior Modeling ◽  
Remote Detection ◽  
Sensor Data ◽  
Fire Behavior Modeling ◽  
Fuel Density ◽  
National Estimates ◽  
Satellite Sensor Data ◽  
Satellite Sensor

Abstract Background National estimates of canopy bulk density (CBD; kg m−3) for fire behavior modeling are generated and supported by the LANDFIRE program. However, locally derived estimates of CBD at finer scales are preferred over national estimates if they exist, as the absolute accuracy of the LANDFIRE CBD product is low and varies regionally. Active sensors (e.g., lidar or radar) are better suited for this task, as passive sensors are ill equipped to detect differences among key vertical fuel structures, such as coniferous surface fuels (≤2 m high) and canopy fuels above this threshold—a key categorical fuel distinction in fire behavior modeling. However, previous efforts to map CBD using lidar sensor data in the Superior National Forest (SNF) of Minnesota, USA, yielded substandard results. Therefore, we use a combination of dormant-season synthetic aperture radar (SAR) and optical satellite sensor data to (1) expand detectability of coniferous fuels among mixed forest canopies to improve the accuracy of CBD modeling and (2) better understand the influence of surface fuels in this regard. Response variables included FuelCalc output and indirect estimates of maximum burnable fuel based on canopy gap fraction (CGF) measured at ground level and 2 m above ground level. Results SAR variables were important predictors of CBD and total fuel density (TFD) in all independent model calibrations with ground data, in which we define TFD as the sum of CBD and primarily live coniferous surface fuel density (SFD) 0 to 2 m above ground. Exploratory estimates of TFD appeared biased to the presence of sapling-stage conifer fuel on measures of CGF at the ground level. Thus, modeling efforts to calibrate SFD with satellite sensor data failed. Both CGF-based and FuelCalc-based field estimates of CBD yielded close unity with satellite-calibrated estimates, although substantial differences in data distributions existed. Estimates of CBD from the widest CGF zenith angle range (0 to 38°) correlated best with FuelCalc-based CBD estimates, while both resulted in maximum biomass values that exceeded those considered typical for the SNF. Model results from the narrowest zenith angle range (0 to 7°) produced estimates of CBD that were more in line with values considered typical. LANDFIRE’s estimates of CBD were weakly, but significantly (P = 0.05), correlated to both narrow- and wide-angle CGF-based estimates of CBD, but not with FuelCalc-based estimates. Conclusions The combined use of field estimates of CBD, based on indirect measures of CGF according to Keane et al. (Canadian Journal of Forest Research 35:724–739, 2005), with SAR and optical satellite sensor data demonstrates the potential of this method for mapping CBD in the Upper Midwest, USA. Results suggested that the presence of live, coniferous surface fuels neither confounds remote detection nor precludes mapping of CBD in this region using SAR satellite sensor data, as C- and L-band idiosyncrasies likely limit the visibility of these smaller understory fuels from space. Nevertheless, research using direct measures of burnable SFD for calibrations with SAR satellite sensor data should be conducted to more definitively answer this remote detection question, as we suspect substantial bias among measures of CGF from ground level when estimating SFD as the difference between TFD and CBD.

scholarly journals Observed versus predicted fire behavior in an Alaskan black spruce forest ecosystem: an experimental fire case study

Fire Ecology ◽  
2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Stacy A. Drury
Keyword(s):  
Black Spruce ◽  
Fire Behavior ◽  
Flame Length ◽  
Behavior Modeling ◽  
Fire Intensity ◽  
Modeling Tools ◽  
Rate Of Spread ◽  
Fire Behavior Modeling ◽  
Modeling Systems ◽  
Hazard And Risk

Abstract Background Fire managers tasked with assessing the hazard and risk of wildfire in Alaska, USA, tend to have more confidence in fire behavior prediction modeling systems developed in Canada than similar systems developed in the US. In 1992, Canadian fire behavior systems were adopted for modeling fire hazard and risk in Alaska and are used by fire suppression specialists and fire planners working within the state. However, as new US-based fire behavior modeling tools are developed, Alaskan fire managers are encouraged to adopt the use of US-based systems. Few studies exist in the scientific literature that inform fire managers as to the efficacy of fire behavior modeling tools in Alaska. In this study, I provide information to aid fire managers when tasked with deciding which system for modeling fire behavior is most appropriate for their use. On the Magitchlie Creek Fire in Alaska, I systematically collected fire behavior characteristics within a black spruce (Picea mariana [Mill.] Britton, Sterns & Poggenb.) ecosystem under head fire conditions. I compared my fire behavior observations including flame length, rate of spread, and head fire intensity with fire behavior predictions from the US fire modeling system BehavePlus, and three Canadian systems: RedAPP, CanFIRE, and the Crown Fire Initiation and Spread system (CFIS). Results All four modeling systems produced reasonable rate of spread predictions although the Canadian systems provided predictions slightly closer to the observed fire behavior. The Canadian fire behavior prediction modeling systems RedAPP and CanFIRE provided more accurate predictions of head fire intensity and fire type than BehavePlus or CFIS. Conclusions The most appropriate fire behavior modeling system for use in Alaskan black spruce ecosystems depends on what type of questions are being asked. For determining the rate of fire movement across a landscape, REDapp, CanFIRE, CFIS, or BehavePlus can all be expected to provide reasonably accurate estimates of rate of spread. If fire managers are interested in using predicted flame length or energy produced for informing decisions such as which firefighting tactics will be successful, or for evaluating the ecological impacts due to burning, then the Canadian fire modeling systems outperformed BehavePlus in this case study.

scholarly journals Fire behavior modeling for operational decision-making

2021 ◽  
pp. 100291
Author(s):  
Adrián Cardil ◽  
Santiago Monedero ◽  
Gavin Schag ◽  
Sergio de-Miguel ◽  
Mario Tapia ◽  
...  
Keyword(s):  
Decision Making ◽  
Fire Behavior ◽  
Behavior Modeling ◽  
Fire Behavior Modeling ◽  
Operational Decision Making

scholarly journals Using the Landsat Burned Area Products to Derive Fire History Relevant for Fire Management and Conservation in the State of Florida, Southeastern USA

Fire ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 26
Author(s):  
Casey Teske ◽  
Melanie K. Vanderhoof ◽  
Todd J. Hawbaker ◽  
Joe Noble ◽  
John Kevin Hiers
Keyword(s):  
Prescribed Fire ◽  
Spatial Data ◽  
Fire History ◽  
Fire Management ◽  
Fire Behavior ◽  
Fire Risk ◽  
Behavior Modeling ◽  
Burned Area ◽  
Burned Areas ◽  
Burn Probability

Development of comprehensive spatially explicit fire occurrence data remains one of the most critical needs for fire managers globally, and especially for conservation across the southeastern United States. Not only are many endangered species and ecosystems in that region reliant on frequent fire, but fire risk analysis, prescribed fire planning, and fire behavior modeling are sensitive to fire history due to the long growing season and high vegetation productivity. Spatial data that map burned areas over time provide critical information for evaluating management successes. However, existing fire data have undocumented shortcomings that limit their use when detailing the effectiveness of fire management at state and regional scales. Here, we assessed information in existing fire datasets for Florida and the Landsat Burned Area products based on input from the fire management community. We considered the potential of different datasets to track the spatial extents of fires and derive fire history metrics (e.g., time since last burn, fire frequency, and seasonality). We found that burned areas generated by applying a 90% threshold to the Landsat burn probability product matched patterns recorded and observed by fire managers at three pilot areas. We then created fire history metrics for the entire state from the modified Landsat Burned Area product. Finally, to show their potential application for conservation management, we compared fire history metrics across ownerships for natural pinelands, where prescribed fire is frequently applied. Implications of this effort include increased awareness around conservation and fire management planning efforts and an extension of derivative products regionally or globally.

scholarly journals Decomposing the Interactions between Fire Severity and Canopy Fuel Structure Using Multi-Temporal, Active, and Passive Remote Sensing Approaches

Fire ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. 7 ◽  
Author(s):  
Nicholas S. Skowronski ◽  
Michael R. Gallagher ◽  
Timothy A. Warner
Keyword(s):  
Laser Scanning ◽  
Treatment Effectiveness ◽  
Fire Severity ◽  
Fire Behavior ◽  
Three Dimensional ◽  
Structural Heterogeneity ◽  
Behavior Modeling ◽  
Fuel Loading ◽  
Subtle Difference ◽  
Multi Temporal

Within the realms of both wildland and prescribed fire, an understanding of how fire severity and forest structure interact is critical for improving fuels treatment effectiveness, quantifying the ramifications of wildfires, and improving fire behavior modeling. We integrated high resolution estimates of fire severity with multi-temporal airborne laser scanning data to examine the role that various fuel loading, canopy shape, and other variables had on predicting fire severity for a complex of prescribed fires and one wildfire and how three-dimensional fuels changed as a result of these fires. Fuel loading characteristics were widely variable, and fires were ignited using a several techniques (heading, flanking, and backing), leading to a large amount of variability in fire behavior and subsequent fire effects. Through our analysis, we found that fire severity was linked explicitly to pre-fire fuel loading and structure, particularly in the three-dimensional distribution of fuels. Fire severity was also correlated with post-fire fuel loading, forest structural heterogeneity, and shifted the diversity and abundance of canopy classes within the landscape. This work demonstrates that the vertical distribution of fuel is an important factor and that subtle difference has defined effects on fire behavior and severity.

scholarly journals Chronosequence of Fuel Loading and Fuel Depth Following Forest Rehabilitation Frill Treatment of Tanoak to Release Douglas-Fir: A Case Study from Northern California

Forests ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 691
Author(s):  
Raven M. Krieger ◽  
Brian E. Wall ◽  
Cody W. Kidd ◽  
John-Pascal Berrill
Keyword(s):  
Woody Debris ◽  
Fire Severity ◽  
Fire Behavior ◽  
Fire Effects ◽  
Behavior Modeling ◽  
Northern California ◽  
Fuel Loading ◽  
Excessive Competition ◽  
Pre Treatment ◽  
Forest Rehabilitation

There is concern that forest management activities such as chemical thinning may increase hazardous fuel loading and therefore increase risk of stand-replacing wildfire. Chemical thinning, often accomplished by frill treatment of unwanted trees, leaves trees standing dead for a time before they fall and become surface fuels. In coastal northern California, frill treatment is used as a forest rehabilitation treatment that removes tanoak (Notholithocarpus densiflorus) to release merchantable conifers from excessive competition. We studied fuel bed depth and fuel loading after frill treatment of tanoak along a 16-year chronosequence that substituted space for time. The total depth of fuel bed was separated into woody fuels, litter, and duff. The height of each layer was variable and greatest on average in post-treatment year 5 after treated tanoak had begun to break apart and fall. Initially, the evergreen tanoak trees retained their foliage for at least a year after treatment. Five years after treatment, many tanoak had fallen and transitioned to become fine- and coarse woody debris. After 11 years, the larger pieces of down wood were mostly classified as rotten. After 16 years, the fuel loading appeared roughly equivalent to pre-treatment levels, however we did not explicitly test for differences due to potential confounding between time and multiple factors such as inter-annual climate variations and site attributes. Nevertheless, our data provide some insight into changes in surface fuel characteristics due to rehabilitation treatments. These data can be used as inputs for fire behavior modeling to generate indicative predictions of fire effects such as fire severity and how these change over time since treatment.

scholarly journals Prediction of Fuel Loading Following Mastication Treatments in Forest Stands in North Idaho, USA

2020 ◽  
Vol 12 (17) ◽  
pp. 7025
Author(s):  
Ryer Becker ◽  
Robert Keefe
Keyword(s):  
Fire Behavior ◽  
Stand Density ◽  
Post Treatment ◽  
Behavior Modeling ◽  
Fuel Reduction ◽  
Fuel Loading ◽  
Stem Size ◽  
Fuel Loads ◽  
Pre Treatment ◽  
Mean Square Errors

Fuel reduction in forests is a high management priority in the western United States and mechanical mastication treatments are implemented common to achieve that goal. However, quantifying post-treatment fuel loading for use in fire behavior modeling to forecast treatment effectiveness is difficult due to the high cost and labor requirements of field sampling methods and high variability in resultant fuel loading within stands after treatment. We evaluated whether pre-treatment LiDAR-derived stand forest characteristics at 20 m × 20 m resolution could be used to predict post-treatment surface fuel loading following mastication. Plot-based destructive sampling was performed immediately following mastication at three stands in the Nez Perce Clearwater National Forest, Idaho, USA, to correlate post-treatment surface fuel loads and characteristics with pre-treatment LiDAR-derived forest metrics, specifically trees per hectare (TPH) and stand density index (SDI). Surface fuel loads measured in the stand post-treatment were consistent with those reported in previous studies. A significant relationship was found between the pre-treatment SDI and total resultant fuel loading (p = 0.0477), though not between TPH and fuel loading (p = 0.0527). SDI may more accurately predict post-treatment fuel loads by accounting for both tree number per unit area and stem size, while trees per hectare alone does not account for variations of tree size and subsequent volume within a stand. Relatively large root-mean-square errors associated with the random forest models for SDI (36%) and TPH (46%) suggest that increased sampling intensity and modified methods that better account for fine spatial variability in fuels resulting from within-stand conditions, treatment prescriptions and machine operators may be needed. Use of LiDAR to predict fuel loading after mastication is a useful approach for managers to understand the efficacy of fuel reduction treatments by providing information that may be helpful for determining areas where treatments can be most beneficial.

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